Export results from cluster to Ensight
Altair EmployeeHi,
I was sent here coming from this topic right here:
I ran a case on our cluster and now want to export the results to Ensight format to postprocess in Hyperview, but AcuOut does not seem to find the results. I tried to open the case (.acs file) in Acusolve and export it using AcuOut. I assume this way only works right after a case as been solved locally. However, I need to export the files coming from another machine. How can one do this? By the way, I can't just open the .log file in Hyperview. I run shear thinning fluid simulations and when I try to open these in HV via the logfile, I get an error saying 'Time does not match' and I can't display all the time steps. Also, velocity and pressure are the only displayed values this way, although I set more than these. So this way is not an option, unfortunately.
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Altair EmployeeIn case you have GUI access on the cluster, you could open acuOut command directly on the cluster and it might be easier than using acuTrans than typing the entire command (to generate H3D).
Altair EmployeeHi,
Thanks for your answer. Acusim.dir exists of course. With the Reader Options tab, I did get all my output options to show up, but I still got the 'Time does not match' error. Until now, it only occured when I ran shear thinning (power law) cases, newtonian setups were no problem. When this happens, the displayed time steps do not match the results I expect. For example, the case at hand was the full geometry I am examining and I already ran parts of it (1 degree, 5 degrees of the rotationally symmetric body), so I know what to expect. The last displayed result of the full body is not what the other cases showed, which I ran on my local machine and exported to Ensight using AcuOut.
I just tried acuTrans. I opened Acusolve CMD prompt, navigated to my case and typed 'acuTrans -translate_to ensight' to test it. Unfortunately, it told me this:
acuTrans: *** ASSERTION in Function <iopCheckStr> File <iopLow.c> Line <2074>
acuTrans: *** data out of order: expected <nAoutVecs> found <nFields> in binary
file <ACUSIM.DIR/acusolve.2_0_x.out> token <8> near <nFields>
Is the message 'data out of order' maybe related to the issue I have described above? As far as I know, there is no GUI access.
Altair EmployeePlease double check to make sure the version of acuTrans you are using is the same version as used for acuSolve.
Altair EmployeeThere is only one version of Acusolve suite installed, which is v13.0
Altair EmployeeCould you try using H3D file format for export to HyperView? There are two alternatives either one H3D file with all time steps or multiple H3D files(for each time step). Please try both and report it either works for you (as there is adaptive time stepping in your setup).
Altair EmployeeHi again,
I was wrong in my previous reply. The version installed on the cluster is 14.0 while my local version is still 13.0, so no wonder there might be compatibility issues. Sorry for that, no more searching needed I guess. I just tried H3D and while it did convert with AcuTrans without errors, Hyperview tells me this after a few messages: Error: Failed to attach results.
Altair EmployeeThis is very likely to be compatibility issue. Is it possible to upgrade to v14?
I am not sure if there is any issue with post-processing of adapative time steps. Is this the same model, you attached at the beginning of this thread?
Altair EmployeeIf you're upgrading, probably best to go to version 2017 - the latest release version. But, again, the version should be consistent both on the cluster where the job is run and on the local machine.
Altair EmployeeI got Acusolve 2017 installed locally an a machine to test a few things. By the way, I went to an Acusolve training session a few months ago and we were told to pick fixed time stepping over adaptive time stepping. After asking why, since every other program I know supports adaptive time stepping without a problem, we were given no details unfortunately. Maybe this was the reason. Also, I just tried opening a test run of AS2017 in Hyperview 14 using the .log file after the same case done with adaptive and fixed time stepping. When I try to apply the velocity contour plot, Hyperview just crashes and closes without a message. Using Ensight is no problem.
Altair EmployeeThere might be new information in AS 2017 results, that was not available with HV14, hence the likely crash. I would suggest to generally use consistent versions for AS and HV.
In order that HyperView reads the results correctly from .Log file, ACUSIM.DIR also needs to be present (That is the actual folder with results).
BTW, Ensight format is not required to read AcuSolve results. Either direct .Log file (with ACUSIM.DIR present) or H3D export.
For the case that you are not able to see the additional quantities, you could use the options in HV to enable reading of extended nodal outputs.See screenshot.
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